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Development of Machine Learning-Driven Dual-Task Gait Test Model for Cognitive Impairment Screening
Mengshu Yang1, Qing Yang2, Gang Xiong3
1School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan; School of Medicine, Xiangyang Polytechnic College, Xiangyang.
Objective:
To develop an artificial intelligence (AI)-aided dual-task gait test model for scalable, high-throughput cognitive impairment screening.
Design:
A diagnostic case-control study between 2022 and 2023, community-dwelling adults aged ≥60 years capable of unaided walking were enrolled.
Setting(S):
Primary care.
Participants:
A total of 201 participants were classified into 3 groups: dementia (n=26), mild cognitive impairment (n=124), and cognitively intact (n=51). All participants underwent 3 gait assessments: single-task, dual-task (serial subtraction by 7), and dual-task (animal-naming) gait test.
Interventions:
Not applicable.
Main Outcome Measures:
A total of 45 gait parameter metrics were extracted using AI-assisted gait analysis. Cognition was adjudicated by an expert panel using cognitive tests, functional status, and clinical records. Logistic regression models were used to analyze the association between gait parameters and cognition, with area under the curve (AUC) used to evaluate diagnostic performance. Four machine learning models were employed for model optimization. Subgroup analyses were conducted to assess model fairness.
Results:
Multiple gait parameters showed significant differences between groups, with gait speed and stride variability emerging as the strongest predictors. After adjustment for covariates, the logistic regression models demonstrated notable screening accuracy for dementia (AUC=0.883; 95% CI, 0.811-0.955) and for cognitive impairment (AUC=0.895; 95% CI, 0.850-0.941). Machine learning-based optimization further validated these findings, with the support vector machine model achieving an AUC of 0.786 (sensitivity: 83.54%, specificity: 78.18%) for dementia and an AUC of 0.808 (sensitivity: 85.54%, specificity: 76.46%) for cognitive impairment. Subgroup analyses were exploratory and constrained by small sample sizes, with results showing considerable variability.
Conclusions:
The AI-aided dual-task gait paradigm is a scalable, cost-effective, and highly accurate tool for population-level cognitive impairment screening in older adults. Its transformative potential lies in enabling early detection in community settings, establishing a cornerstone for proactive dementia prevention through timely interventions.
